The methods generally used are based on the search for a word A.sup.m which is nearest to a data word X to be processed. The word A.sup.m belongs to a group of M reference words which are called a dictionary. These words A.sup.m and X generally comprise N components A.sub.i.sup.m (or X.sub.i) where i=1..N.
The proximity is often evaluated on the basis of a distance criterion d(A.sup.m,X) between the word A.sup.m and the word X. When the components are numbers, for example the euclidean distance can be taken: ##EQU1##
Thus, it is necessary to determine the word A.sup.m1 for which the distance from the data word X is minimum. Thus, for any word A.sup.m of the dictionary it is necessary that: EQU d(A.sup.m1,X).ltoreq.d(A.sup.m,X)
It is not only possible to determine the one nearest word A.sup.m1, but also the K words A.sup.m1, A.sup.m2, . . . , A.sup.mK which are nearest with respect to a predetermined proximity limit. The search may be terminated, either when a sufficient number of corresponding words has been found or when all corresponding words have been found that correspond better than or at least as well as according to a correspondence limit.
The method currently used for the determination of the nearest word or words consists in the execution of a complete calculation of all distances between a data word and all respective words of the dictionary, followed by a search for the smallest distance or distances by way of a comparison operation. Such an operation enables an error-free result to be obtained. However, it is very costly in terms of calculation time, i.e. mainly for the calculation of all distances when the dictionary contains many words.
Various proposals have been made for reducing this calculation duration, but generally they all lead to either the introduction of errors or the imposition of constraints as regards the dictionary. A relevant reference is: "Delayed-decision binary tree-searched vector quantization for image compression" by CHIA LUNG YEH, SPIE Vol. 1989, pp. 154-158. The document proposes the execution of a tree-search on the words of the dictionary by examining several branches of said tree simultaneously. This method determines a code with few errors when the number of branches examined is large, but requires a long calculation time.
The problem to be solved, therefore, is the selection of the word or words of the dictionary which is or are nearest to a data word to be analysed, notably by reducing the duration of processing. It should be possible to carry out this operation with an arbitrarily low error rate.